Journal articles: 'Task allocation to sensors'

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Hiraga, Motoaki, Toshiyuki Yasuda, and Kazuhiro Ohkura. "Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 621–28. http://dx.doi.org/10.20965/jaciii.2018.p0621.

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Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

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Yin, Xiang, Kaiquan Zhang, Bin Li, Arun Kumar Sangaiah, and Jin Wang. "A task allocation strategy for complex applications in heterogeneous cluster–based wireless sensor networks." International Journal of Distributed Sensor Networks 14, no. 8 (August 2018): 155014771879535. http://dx.doi.org/10.1177/1550147718795355.

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To a wireless sensor network, cooperation among multiple sensors is necessary when it executes applications that consist of several computationally intensive tasks. Most previous works in this field concentrated on energy savings as well as load balancing. However, these schemes merely considered the situations where only one type of resource is required which drastically constrains their practical applications. To alleviate this limitation, in this article, we investigate the issue of complex application allocation, where various distinctive types of resources are demanded. We propose a heuristic-based algorithm for distributing complex applications in clustered wireless sensor networks. The algorithm is partitioned into two phases, in the inter-cluster allocation stage, tasks of the application are allocated to various clusters with the purpose of minimizing energy consumption, and in the intra-cluster allocation stage, the task is distributed to appropriate sensor nodes with the consideration of both energy cost and workload balancing. In so doing, the energy dissipation can be reduced and balanced, and the lifetime of the system is extended. Simulations are conducted to evaluate the performance of the proposed algorithm, and the results demonstrate that the proposed algorithm is superior in terms of energy consumption, load balancing, and efficiency of task allocation.

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Zha, Zhihua, Chaoqun Li, Jing Xiao, Yao Zhang, Hu Qin, Yang Liu, Jie Zhou, and Jie Wu. "An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs." Journal of Sensors 2021 (April 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5582646.

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Xu, Haitao, Hongjie Gao, Chengcheng Zhou, Ruifeng Duan, and Xianwei Zhou. "Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting." Sensors 19, no. 23 (November 22, 2019): 5115. http://dx.doi.org/10.3390/s19235115.

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The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

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Bagherinia, Ali. "Optimized Task Allocation in Sensor Networks." International Journal of Information Technology, Modeling and Computing 1, no. 3 (August 31, 2013): 43–49. http://dx.doi.org/10.5121/ijitmc.2013.1305.

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Elmogy, Ahmed M., Alaa M. Khamis, and Fakhri O. Karray. "Market-Based Approach to Mobile Surveillance Systems." Journal of Robotics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/841291.

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The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

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Semnani, Samaneh Hosseini, and Otman A. Basir. "Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks." Unmanned Systems 05, no. 01 (January 2017): 31–43. http://dx.doi.org/10.1142/s2301385017500030.

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Efficient use of the network’s resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network’s success in tracking targets, efficiently and effectively, hinges significantly on the network’s ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multi-sensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

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Stanulovic, Jelena, Nathalie Mitton, and Ivan Mezei. "Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN." Sensors 21, no. 18 (September 13, 2021): 6149. http://dx.doi.org/10.3390/s21186149.

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Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots’ remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.

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He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.

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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.

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Zhu, Xiaojuan, Kuan-Ching Li, Jinwei Zhang, and Shunxiang Zhang. "Distributed Reliable and Efficient Transmission Task Assignment for WSNs." Sensors 19, no. 22 (November 18, 2019): 5028. http://dx.doi.org/10.3390/s19225028.

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Task assignment is a crucial problem in wireless sensor networks (WSNs) that may affect the completion quality of sensing tasks. From the perspective of global optimization, a transmission-oriented reliable and energy-efficient task allocation (TRETA) is proposed, which is based on a comprehensive multi-level view of the network and an evaluation model for transmission in WSNs. To deliver better fault tolerance, TRETA dynamically adjusts in event-driven mode. Aiming to solve the reliable and efficient distributed task allocation problem in WSNs, two distributed task assignments for WSNs based on TRETA are proposed. In the former, the sink assigns reliability to all cluster heads according to the reliability requirements, so the cluster head performs local task allocation according to the assigned phase target reliability constraints. Simulation results show the reduction of the communication cost and latency of task allocation compared to centralized task assignments. Like the latter, the global view is obtained by fetching local views from multiple sink nodes, as well as multiple sinks having a consistent comprehensive view for global optimization. The way to respond to local task allocation requirements without the need to communicate with remote nodes overcomes the disadvantages of centralized task allocation in large-scale sensor networks with significant communication overheads and considerable delay, and has better scalability.

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Almudayni, Ziyad, Ben Soh, and Alice Li. "IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks." Future Internet 16, no. 3 (March 8, 2024): 93. http://dx.doi.org/10.3390/fi16030093.

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The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA’s efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.

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Yin, Xiang, Weichao Dai, Bin Li, Liping Chang, and Chunxiao Li. "Cooperative task allocation in heterogeneous wireless sensor networks." International Journal of Distributed Sensor Networks 13, no. 10 (October 2017): 155014771773574. http://dx.doi.org/10.1177/1550147717735747.

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Yang, Jian, and Xuejun Huang. "A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile Information." Applied Sciences 13, no. 6 (March 14, 2023): 3705. http://dx.doi.org/10.3390/app13063705.

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Cluster formation and task processing are standard features for leveraging the performance of unmanned aerial vehicles (UAVs). As the UAV network is aided by sensors, functions such as clustering, reformation, and autonomous working are adaptively used for dense task processing. In consideration of the distributed nature of the UAV network coupled with wireless sensors, this article introduces a Rational Clustering Method (RCM) using dense task neighbor information exchange. The Rational Clustering Method (RCM) is an algorithm for dense task neighbor information exchange that can be used to cluster objects according to their shared properties. Each object’s task neighbors, and the similarities between them, are calculated using this method. Starting with the task density of its neighbors, the RCM algorithm gives each object in the dataset a weight. This information exchange process identifies a UAV units’ completing tasks and free slots. Using this information, high-slot UAVs within the communication range can be grouped as clusters. Unlike wireless sensor clusters, task allocation is performed on the basis of available slots and UAV longevity within the cluster; this prevents task incompletion/failures and delays in a densely populated UAV scenario. Cluster sustainability or dispersion is recommended when using distributed state learning. State learning transits between the pending task and UAV longevity; an intermediate state is defined for task reassignment amid immediate cluster deformation. This triple feature-based distributed method balances tasks between failures, overloading, and idle UAVs. The RCM was verified using task processing rate, completion ratio, reassignment, failures, and delay. Task processing rate was increased by 8.16% and completion ratio was increased by 10.3% with the proposed RCM-IE. Reassignment, failure, and delay were all reduced by 12.5%, 9.87%, and 11.99%, respectively, using this method.

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Ipaye, Aridegbe A., Zhigang Chen, Muhammad Asim, Samia Allaoua Chelloug, Lin Guo, Ali M. A. Ibrahim, and Ahmed A. Abd El-Latif. "Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm." Sensors 22, no. 8 (April 14, 2022): 3013. http://dx.doi.org/10.3390/s22083013.

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Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

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Al-Buraiki, Omar, Wenbo Wu, and Pierre Payeur. "Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks." Robotics 9, no. 3 (July 16, 2020): 54. http://dx.doi.org/10.3390/robotics9030054.

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Task allocation for specialized unmanned robotic agents is addressed in this paper. Based on the assumptions that each individual robotic agent possesses specialized capabilities and that targets representing the tasks to be performed in the surrounding environment impose specific requirements, the proposed approach computes task-agent fitting probabilities to efficiently match the available robotic agents with the detected targets. The framework is supported by a deep learning method with an object instance segmentation capability, Mask R-CNN, that is adapted to provide target object recognition and localization estimates from vision sensors mounted on the robotic agents. Experimental validation, for indoor search-and-rescue (SAR) scenarios, is conducted and results demonstrate the reliability and efficiency of the proposed approach.

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Dai, Liang, Yilin Chang, and Zhong Shen. "A Non-cooperative Game Algorithm for Task Scheduling in Wireless Sensor Networks." International Journal of Computers Communications & Control 6, no. 4 (December 1, 2011): 592. http://dx.doi.org/10.15837/ijccc.2011.4.2087.

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Scheduling tasks in wireless sensor networks is one of the most challenging problems. Sensing tasks should be allocated and processed among sensors in minimum times, so that users can draw prompt and effective conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. But sensors may refuse to take pains to carry out the tasks due to the limited energy. To solve the potentially selfish problem of the sensors, a non-cooperative game algorithm (NGTSA) for task scheduling in wireless sensor networks is proposed. In the proposed algorithm, according to the divisible load theory, the tasks are distributed reasonably to every node from SINK based on the processing capability and communication capability. By removing the performance degradation caused by communications interference and idle, the reduced task completion time and the improved network resource utilization are achieved. Strategyproof mechanism which provide incentives to the sensors to obey the prescribed algorithms, and to truthfully report their parameters, leading to an effient task scheduling and execution. A utility function related with the total task completion time and tasks allocating scheme is designed. The Nash equilibrium of the game algorithm is proved. The simulation results show that with the mechanism in the algorithm, selfish nodes can be forced to report their true processing capability and endeavor to participate in the measurement, thereby the total time for accomplishing the task is minimized and the energy-consuming of the nodes is balanced.

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Umer, Asif, Mushtaq Ali, Ali Imran Jehangiri, Muhammad Bilal, and Junaid Shuja. "Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics." Sensors 24, no. 8 (April 9, 2024): 2381. http://dx.doi.org/10.3390/s24082381.

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IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes’ priority for task allocation and scheduling. The AHP decides the fog nodes’ priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

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Xu, Mengying, and Jie Zhou. "Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks." Journal of Sensors 2020 (January 31, 2020): 1–9. http://dx.doi.org/10.1155/2020/3231864.

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The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

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Veerapathiran, Sasireka, and Shyamala Ramachandran. "A Multi Task Allocation Based Time Optimization Framework Using Social Networks in Mobile Crowd Sensing." Instrumentation Mesure Métrologie 21, no. 6 (December 31, 2022): 237–41. http://dx.doi.org/10.18280/i2m.210605.

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The quality of data and its sensing cost is the important concern for task allocation in crowd sensing. The sensing capabilities of a device to send the collection of sensor data to a cloud requires crowd sensing in order to receive reliable data. Crowd sensing is used in many areas such as traffic monitoring, smart cities, health care, transportations, environmental monitoring and many more. Most of existing works are only based on assumptions in task scheduling about the number of candidate users and are mainly performed optimization of single task allocation. If the candidate users are few, then the completion of task with in the schedule can be difficult for many sensor applications. In this work, we proposed a social network-based task allocation scheme for the optimization of multi task allocation. The main idea of the proposed work is to maximize the task completion within the allocated schedule. It is evident that the task scheduling algorithms are NP-hard and we introduced a decreasing threshold task allocation (DTT) and fast greedy selections (FGS) algorithms along with Crow COOT Foraging Optimization (CCFO) algorithm to allocate the tasks parallelly with maximum efficiency. The proposed algorithms such as C-DTT (CCFO-DTT) and C-FGS (CCFO-FGS_ are used for the efficient allocation of tasks. The combination of these algorithms can be helpful in selecting the candidate users who will perform the completion of maximum tasks. Due to the selection of proper users in each round, the time consumption of the tasks to be completed is greatly reduced. The experimental results also indicates that the proposed work performs well in the optimization of multi task allocation than the other state of the art models.

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Shi, Guoqing, Fan Wu, Lin Zhang, Shuyang Zhang, and Cao Guo. "An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 722–27. http://dx.doi.org/10.1051/jnwpu/20183640722.

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The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

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Alfakeeh, Ahmed S., and Muhammad Awais Javed. "Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks." Mathematics 11, no. 17 (September 4, 2023): 3798. http://dx.doi.org/10.3390/math11173798.

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Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

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Wahn, Basil, and Peter König. "Is Attentional Resource Allocation Across Sensory Modalities Task-Dependent?" Advances in Cognitive Psychology 13, no. 1 (March 31, 2017): 83–96. http://dx.doi.org/10.5709/acp-0209-2.

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Tkach, Itshak, Yael Edan, and Shimon Y. Nof. "Multi-sensor task allocation framework for supply networks security using task administration protocols." International Journal of Production Research 55, no. 18 (February 6, 2017): 5202–24. http://dx.doi.org/10.1080/00207543.2017.1286047.

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Xie, Zhenzhen, Liang Hu, Yan Huang, and Junjie Pang. "A Semiopportunistic Task Allocation Framework for Mobile Crowdsensing with Deep Learning." Wireless Communications and Mobile Computing 2021 (February 15, 2021): 1–15. http://dx.doi.org/10.1155/2021/6643229.

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The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high-quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept-based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff-maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning- (RL-) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.

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Fei, Hongmei, Baitao Zhang, Yan Liu, Manli Yan, Yi Lu, and Jie Zhou. "A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks." Applied Sciences 13, no. 17 (August 31, 2023): 9870. http://dx.doi.org/10.3390/app13179870.

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In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.

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Wang, Ruyan, Ailing Zhong, Zhidu Li, Hong Zhang, and Xingjie Li. "Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks." Sensors 21, no. 1 (December 27, 2020): 121. http://dx.doi.org/10.3390/s21010121.

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How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

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Rai, Anjani, and Ashish Sharma. "Hybrid Cluster Algorithm and Task Allocation Optimization for Improving Percolation of Multi-targets in Wireless Sensor Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 655–65. http://dx.doi.org/10.5373/jardcs/v11sp10/20192855.

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Gul, Omer Melih. "Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks." Sensors 23, no. 6 (March 20, 2023): 3284. http://dx.doi.org/10.3390/s23063284.

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In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

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Fu, Yanming, Xiao Liu, Weigeng Han, Shenglin Lu, Jiayuan Chen, and Tianbing Tang. "Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation." Electronics 12, no. 16 (August 15, 2023): 3454. http://dx.doi.org/10.3390/electronics12163454.

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With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

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Yang, Huifeng, Xianglong Meng, Yichao Li, Yong Wei, Li Shang, Jiucheng Wang, and Peng Lin. "Joint Virtual Machine Selection and Computation Resource Allocation in Mobile Edge Computing." Journal of Sensors 2023 (September 27, 2023): 1–11. http://dx.doi.org/10.1155/2023/5514655.

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Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored during the task computation period. In this work, we aim to minimize the energy consumption of SPSs subject to the task offloading delay by jointly optimizing the VM selection and computation resource allocation. Considering the formulated problem is nonconvex, we first utilize the linearization method to transform it into a convex optimization problem. And then, by using the branch and bound method, we propose the joint VM selection and computation resource allocation (JVMSRA) algorithm. Considering the complexity of the JVMSRA algorithm is high, we decompose the primal problem into two subproblems and solve them by utilizing the ant colony method and CVX package, respectively. Based on the solutions of the two subproblems, the resource allocation-based ant colony (RAAC) algorithm is proposed. Simulation results show that the proposed RAAC algorithm and JVMSRA algorithm decrease by 6% and 8.8% on average compared with the benchmark algorithm, respectively, when the computation resources of each VM increase from 1 to 3 GHz.

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Tkach, Itshak, Aleksandar Jevtić, Shimon Nof, and Yael Edan. "A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation." Sensors 18, no. 3 (March 2, 2018): 759. http://dx.doi.org/10.3390/s18030759.

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Lan, Kaixin, Bohao Duan, Shichao Qiu, Yang Xiao, Meng Liu, and Haocen Dai. "Task Allocation and Traffic Route Optimization in Hybrid Fire-fighting Unmanned Aerial Vehicle Network." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 340–55. http://dx.doi.org/10.54097/hset.v9i.1864.

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With the increase of extreme weather conditions in the world, the probability of forest fires is increasing. How the forest fire management decision-making system can monitor and control the fire quickly and effectively is the key of forest fire fighting work. This paper uses SSA drones carrying high-definition and thermal imaging cameras and telemetry sensors in conjunction, as well as Repeater drones used to greatly expand the frontline low-power radio range, to support fire management decision-making systems. At the same time, explore a drone cooperation plan to deal with different fire terrains and different scales of fire conditions. The aim of this paper is to improve the existing fire management decision system in order to quickly respond to the emergency fire. Research object for the Australian state of Victoria on October 1, 2019 to January 7, 2020 wildfires, explore SSA drones and Repeater drones in the application of the forest fire, ensure that fire management decision-making system to provide the optimal number deployment scheme of fire task quickly and efficiently, and achieve the maximum efficiency and economic optimal compatibility.

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Liu, Xinlin, Tian Jing, and Linyi Hou. "An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment." Mathematics 11, no. 10 (May 22, 2023): 2400. http://dx.doi.org/10.3390/math11102400.

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The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

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Dai, Liang, Yilin Chang, and Zhong Shen. "An Optimal Task Scheduling Algorithm in Wireless Sensor Networks." International Journal of Computers Communications & Control 6, no. 1 (March 1, 2011): 101. http://dx.doi.org/10.15837/ijccc.2011.1.2205.

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Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. To minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (OTSA-WSN) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. OTSA-WSN builds from eliminating transmission collisions and idle gaps between two successive data transmissions. By removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. With the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. Finally, simulation results are presented to demonstrate the impacts of different network parameters such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption.

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Wöstmann, Malte, Erich Schröger, and Jonas Obleser. "Acoustic Detail Guides Attention Allocation in a Selective Listening Task." Journal of Cognitive Neuroscience 27, no. 5 (May 2015): 988–1000. http://dx.doi.org/10.1162/jocn_a_00761.

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The flexible allocation of attention enables us to perceive and behave successfully despite irrelevant distractors. How do acoustic challenges influence this allocation of attention, and to what extent is this ability preserved in normally aging listeners? Younger and healthy older participants performed a masked auditory number comparison while EEG was recorded. To vary selective attention demands, we manipulated perceptual separability of spoken digits from a masking talker by varying acoustic detail (temporal fine structure). Listening conditions were adjusted individually to equalize stimulus audibility as well as the overall level of performance across participants. Accuracy increased, and response times decreased with more acoustic detail. The decrease in response times with more acoustic detail was stronger in the group of older participants. The onset of the distracting speech masker triggered a prominent contingent negative variation (CNV) in the EEG. Notably, CNV magnitude decreased parametrically with increasing acoustic detail in both age groups. Within identical levels of acoustic detail, larger CNV magnitude was associated with improved accuracy. Across age groups, neuropsychological markers further linked early CNV magnitude directly to individual attentional capacity. Results demonstrate for the first time that, in a demanding listening task, instantaneous acoustic conditions guide the allocation of attention. Second, such basic neural mechanisms of preparatory attention allocation seem preserved in healthy aging, despite impending sensory decline.

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Huang, Haitao, Min Tian, Jie Zhou, and Xiang Liu. "Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm." Mathematical Biosciences and Engineering 20, no. 8 (2023): 14675–98. http://dx.doi.org/10.3934/mbe.2023656.

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<abstract> <p>Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively.</p> </abstract>

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Qiu, Lanxin, Yi Zhang, Yanbo Wang, Yineng Shen, Jiantao Yuan, and Rui Yin. "Resource Optimization in MEC-Assisted Multirobot Cooperation Systems." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/1377225.

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With prevalent utilization of multirobot cooperation (MRC) systems, people pay more attention to improve the system performance. Among them, the energy consumption and implementation latency of MRC systems are major concerns, and mobile edge computing (MEC) provides a potential way to solve these problems. Therefore, how to leverage MEC to get the balance between computing and communication consumption in MRC systems needs to be investigated urgently. In this paper, a MRC system deployed to accomplish multiple time-critical tasks by MEC technology is studied. The proposed MRC system includes a powerful master robot (MR) and several slave robots (SRs). As a scheduler, MR is responsible for allocating tasks to SRs and has more computing power. SRs are robots with sensors that interact with the environment. In this paper, we propose a strategy for task allocation and resource management in MRC systems. The results show that the proposed scheme can effectively reduce the total energy consumption in SRs.

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Almudayni, Ziyad, Ben Soh, and Alice Li. "Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal." Sensors 23, no. 16 (August 20, 2023): 7286. http://dx.doi.org/10.3390/s23167286.

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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Ding, Xingjian, JianXiong Guo, Yongcai Wang, Deying Li, and Weili Wu. "Task-driven charger placement and power allocation for wireless sensor networks." Ad Hoc Networks 119 (August 2021): 102556. http://dx.doi.org/10.1016/j.adhoc.2021.102556.

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40

ZHU, Jing-Hua. "An Energy Efficient Algorithm for Task Allocation in Wireless Sensor Networks." Journal of Software 18, no. 5 (2007): 1198. http://dx.doi.org/10.1360/jos181198.

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Yu, Yang, and Viktor K. Prasanna. "Energy-Balanced Task Allocation for Collaborative Processing in Wireless Sensor Networks." Mobile Networks and Applications 10, no. 1/2 (February 2005): 115–31. http://dx.doi.org/10.1023/b:mone.0000048550.31717.c5.

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Kian Hsiang Low, W. K. Leow, and M. H. Ang. "Autonomic mobile sensor network with self-coordinated task allocation and execution." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 3 (May 2006): 315–27. http://dx.doi.org/10.1109/tsmcc.2006.871590.

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Yang, Guisong, Zhao Zhang, Jiangtao Wang, and Xingyu He. "Task allocation based on node pair intimacy in wireless sensor networks." IET Communications 14, no. 12 (July 28, 2020): 1902–9. http://dx.doi.org/10.1049/iet-com.2019.0835.

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Yu, Wanli, Yanqiu Huang, and Alberto Garcia-Ortiz. "Distributed Optimal On-Line Task Allocation Algorithm for Wireless Sensor Networks." IEEE Sensors Journal 18, no. 1 (January 1, 2018): 446–58. http://dx.doi.org/10.1109/jsen.2017.2768659.

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Guo, Wenzhong, Ying Chen, and Guolong Chen. "Dynamic Task Scheduling Strategy with Game Theory in Wireless Sensor Networks." New Mathematics and Natural Computation 10, no. 03 (August 21, 2014): 211–24. http://dx.doi.org/10.1142/s1793005714500124.

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Task allocation and scheduling is an important typical problem in the area of high performance computing. Unfortunately, the existing traditional solutions to this problem in high performance computing cannot be directly implemented in wireless sensor networks (WSNs) due to the limitations of WSNs such as resource availability and shared communication medium. In this paper, a dynamic task scheduling strategy with the application of the game theory in WSNs is presented. First, an effective parallel alliance generating algorithm is proposed to process the multi-tasks environment. A task allocation algorithm based on the game theory is used to enhance the performance of the network. A novel resource conflict eliminating algorithm is also developed to eliminate the conflicting issues. Finally, the simulation results confirm and reassure the effectiveness of our proposed scheme as we compare with that of the other schema's available in the public domain.

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Salim, Ahmed, and Akram El Khatib. "An Evaluation of Mobility Effect on Tiny Service Discovery Protocol for Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 1 (November 6, 2014): 5312–22. http://dx.doi.org/10.24297/ijct.v14i1.2125.

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In mobile sensors environment, nodes change their positions this leads to crucially affects service discovery of wireless sensor networks (WSNs). Therefor the accuracy of most service discovery can be limited to just small area of movement, or demands considerable maintenance efforts in term of neighbor nodes allocation. A large waiting time of wireless sensor applications spent in node discovery, as nodes need to periodically advertise their presence and be awake to discover other nodes for services. The optimization of waiting time, which is generally a hard task in static wireless sensor networks, is even harder in mobile wireless sensor networks, where the neighboring nodes also change over time. In this paper, the effect of node mobility on the performance of Tiny Service Discovery Protocol (TinySDP) in WSNs has been analyze. In order to measure and evaluate the performance of TinySDP in mobile WSNs (MWSN), three major metrics of evaluation has been considered such as, Success ratio, Number of transmitted messages and Average waiting time. Simulation results show that the success ratio and average waiting time of TinySDP in mobile WSNs had decreased and the number of transmitted message increased.

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YUFIK, YAN M., and RAJ P. MALHOTRA. "INFORMATION BLENDING IN VIRTUAL ASSOCIATIVE NETWORKS: A NEW PARADIGM FOR SENSOR INTEGRATION." International Journal on Artificial Intelligence Tools 08, no. 03 (September 1999): 275–90. http://dx.doi.org/10.1142/s0218213099000191.

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Research reported in this article is motivated, in part, by current U.S. military programs aimed at the development of efficient data integration and sensor management methods capable of handling large sensor suites and achieving robust target recognition performance in real time scenarios. Modern sensor systems have shown good recognition abilities against a few isolated targets. However, these capabilities decline steeply when multiple sensors are acting against large target groups under realistic conditions requiring dynamic allocation of the sensor resources and efficient on-line integration and disambiguation of multiple sensor outputs. Neural networks and other sensor integration technologies have been inspired by cognitive models attributing human perceptual integration to parallel processing and convergence of simultaneous data streams. This article explores a different model emphasizing serial processing and association of consecutive memory traces in the Long Term Memory (LTM) into a globally connected memory structure called a Virtual Associative Network (VAN). Information integration in VAN is called blending. Target representation is constructed dynamically from the segments of virtual net matched serially against the input segments in the Short Term Memory (STM). This article will elaborate the concept of blending, reference its biological foundations, explain the difference between information blending and conventional sensor fusion techniques, and demonstrate blending applications in a large scale sensor management task.

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Dikmen, Murat, Yeti Li, Philip Farrell, Geoffrey Ho, Shi Cao, and Catherine Burns. "The Effects of Automation and Role Allocation on Team Performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 235–39. http://dx.doi.org/10.1177/1071181319631501.

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An experiment was conducted to investigate the effects of automation and role allocation on performance in a simulated picture compilation task with fourteen two-person student teams. In the absence of automation support, the system integrated sensor information. In the presence of automation support, the system both integrated sensor information and identified contacts. Roles were assigned either based on warfare domain or geographical sectors. Results showed that human-automation system performance was similar in two automation conditions, but participants were slower in classifying tracks and overall classified fewer tracks when the automation was present. We conclude that working with automation may lead to degraded team performance due to complacency and additional task complexity.

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Robbins, Ellie, Joe Nah, Dick Dubbelde, and Sarah Shomstein. "The Costly Influence of Task-Irrelevant Semantic Information on Attentional Allocation." Journal of Vision 20, no. 11 (October 20, 2020): 1525. http://dx.doi.org/10.1167/jov.20.11.1525.

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Jaunzemis, Andris D., Karen M. Feigh, Marcus J. Holzinger, Dev Minotra, and Moses W. Chan. "Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness." Journal of Cognitive Engineering and Decision Making 14, no. 1 (September 26, 2019): 3–33. http://dx.doi.org/10.1177/1555343419872050.

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Existing approaches for sensor network tasking in space situational awareness (SSA) rely on techniques from the 1950s and limited application areas while also requiring significant human-in-the-loop involvement. Increasing numbers of space objects, sensors, and decision-making needs create a demand for improved methods of gathering and fusing disparate information to resolve hypotheses about the space object environment. This work focuses on the cognitive work in SSA sensor tasking approaches. The application of a cognitive work analysis for the SSA domain highlights capabilities and constraints inherent to the domain that can drive SSA operations toward decision-maker goals. A control task analysis is also conducted to derive requirements for cognitive work and information relationships that support the information fusion and sensor allocation tasks of SSA. A prototype decision-support system is developed using a subset of the derived requirements. This prototype is evaluated in a human-in-the-loop experiment using both a hypothesis-based and covariance-based scheduling approaches. Results from this preliminary evaluation show operator ability to address SSA decision-maker hypotheses using the prototype decision-support system (DSS) using both scheduling approaches.

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Journal articles on the topic 'Task allocation to sensors'

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